Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

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An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes

2018 Power Systems Computation Conference (PSCC) ◽

10.23919/pscc.2018.8442916 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yue Cui ◽

Pramod Bangalore ◽

Lina Bertling Tjernberg

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Anomaly Detection ◽

Condition Monitoring ◽

Wind Turbines ◽

Detection Approach ◽

Artificial Neural

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Application and performance of convolutional neural networks to SAR

Radar Sensor Technology XXII ◽

10.1117/12.2305852 ◽

2018 ◽

Author(s):

Ram M. Narayanan ◽

Maxine R. Fox

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

And Performance

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Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-Convolutional Neural Networks Classification

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3891 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2907-2917

Author(s):

P. V. Deepa ◽

S. Joseph Jawhar ◽

J. Merry Geisa

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Brain Tumours ◽

Malignant Tumour ◽

Training Data ◽

Correct Identification ◽

Imaging Model ◽

And Performance ◽

Percent Accuracy ◽

High Level

The field of nanotechnology has lately acquired prominence according to the raised level of correct identification and performance in the patients using Computer-Aided Diagnosis (CAD). Nano-scale imaging model enables for a high level of precision and accuracy in determining if a brain tumour is malignant or benign. This contributes to people with brain tumours having a better standard of living. In this study, We present a revolutionary Semantic nano-segmentation methodology for the nanoscale classification of brain tumours. The suggested Advanced-Convolutional Neural Networks-based Semantic Nano-segmentation will aid radiologists in detecting brain tumours even when lesions are minor. ResNet-50 was employed in the suggested Advanced-Convolutional Neural Networks (A-CNN) approach. The tumour image is partitioned using Semantic Nano-segmentation, that has averaged dice and SSIM values of 0.9704 and 0.2133, correspondingly. The input is a nano-image, and the tumour image is segmented using Semantic Nano-segmentation, which has averaged dice and SSIM values of 0.9704 and 0.2133, respectively. The suggested Semantic nano segments achieves 93.2 percent and 92.7 percent accuracy for benign and malignant tumour pictures, correspondingly. For malignant or benign pictures, The accuracy of the A-CNN methodology of correct segmentation is 99.57 percent and 95.7 percent, respectively. This unique nano-method is designed to detect tumour areas in nanometers (nm) and hence accurately assess the illness. The suggested technique’s closeness to with regard to True Positive values, the ROC curve implies that it outperforms earlier approaches. A comparison analysis is conducted on ResNet-50 using testing and training data at rates of 90%–10%, 80%–20%, and 70%–30%, corresponding, indicating the utility of the suggested work.

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MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea8040038 ◽

2018 ◽

Vol 8 (4) ◽

pp. 38 ◽

Cited By ~ 5

Author(s):

Arjun Pal Chowdhury ◽

Pranav Kulkarni ◽

Mahdi Nazm Bojnordi

Keyword(s):

Neural Networks ◽

Mobile Devices ◽

Convolutional Neural Networks ◽

Mobile User ◽

Power Performance ◽

Performance Requirements ◽

System Energy ◽

And Performance ◽

The Cost

Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time.

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